##
## Call:
## lm(formula = log1p(consumption[1, 7, ]) ~ log1p(socioeconomic[1,
## 1, ]))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6024 -0.5795 -0.0054 0.6270 1.8318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.29966 0.75872 -5.667 1.39e-06 ***
## log1p(socioeconomic[1, 1, ]) 0.70699 0.08213 8.608 1.21e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9621 on 40 degrees of freedom
## Multiple R-squared: 0.6494, Adjusted R-squared: 0.6407
## F-statistic: 74.1 on 1 and 40 DF, p-value: 1.209e-10
##
## Call:
## lm(formula = logYn ~ logPn + logP0, na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7448 -1.0831 0.0005 0.8385 4.4581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.76958 0.20193 8.763 <2e-16 ***
## logPn 3.99850 0.14479 27.616 <2e-16 ***
## logP0 -0.17666 0.01981 -8.916 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.447 on 2513 degrees of freedom
## Multiple R-squared: 0.2341, Adjusted R-squared: 0.2335
## F-statistic: 384 on 2 and 2513 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8605 -0.3927 -0.0772 0.3256 5.1536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.659e-16 1.233e-02 0.00 1
## x 7.480e-01 1.242e-02 60.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.659 on 2854 degrees of freedom
## Multiple R-squared: 0.5595, Adjusted R-squared: 0.5594
## F-statistic: 3626 on 1 and 2854 DF, p-value: < 2.2e-16
## [1] "2 clusters with size of:"
## [1] 38 4
## Republic of Korea Japan India
## 1 1 1
## United States of America Brazil China
## 1 1 1
## Turkey Canada Belgium
## 1 1 1
## Bulgaria Czechia Denmark
## 1 1 2
## Germany Estonia Ireland
## 1 2 2
## Greece Spain France
## 1 1 1
## Italy Cyprus Latvia
## 1 1 1
## Lithuania Luxembourg Hungary
## 1 1 1
## Malta Netherlands Austria
## 1 1 1
## Poland Portugal Romania
## 1 1 1
## Slovenia Slovakia Finland
## 2 1 1
## Sweden United Kingdom Croatia
## 1 1 1
## Iceland Norway North Macedonia
## 1 1 1
## Egypt Mexico China, Hong Kong SAR
## 1 1 1
##
## Call:
## lm(formula = logCspt ~ logPop, na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.50349 -0.10973 0.00066 0.08226 0.77572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.125588 0.007099 17.69 <2e-16 ***
## logPop 0.836460 0.010722 78.02 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.16 on 2514 degrees of freedom
## Multiple R-squared: 0.7077, Adjusted R-squared: 0.7076
## F-statistic: 6086 on 1 and 2514 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = as.vector(scale(log10(consumption[, 7, ])[, -c(20,
## 23, 25, 37, 38)])) ~ as.vector(scale(log10(socioeconomic[,
## 1, ]))[, -c(20, 23, 25, 37, 38)]), na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.45757 -0.24113 -0.00673 0.19729 2.81870
##
## Coefficients:
## Estimate
## (Intercept) -1.349e-15
## as.vector(scale(log10(socioeconomic[, 1, ]))[, -c(20, 23, 25, 37, 38)]) 8.825e-01
## Std. Error
## (Intercept) 9.310e-03
## as.vector(scale(log10(socioeconomic[, 1, ]))[, -c(20, 23, 25, 37, 38)]) 9.379e-03
## t value
## (Intercept) 0.0
## as.vector(scale(log10(socioeconomic[, 1, ]))[, -c(20, 23, 25, 37, 38)]) 94.1
## Pr(>|t|)
## (Intercept) 1
## as.vector(scale(log10(socioeconomic[, 1, ]))[, -c(20, 23, 25, 37, 38)]) <2e-16
##
## (Intercept)
## as.vector(scale(log10(socioeconomic[, 1, ]))[, -c(20, 23, 25, 37, 38)]) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.467 on 2514 degrees of freedom
## Multiple R-squared: 0.7789, Adjusted R-squared: 0.7788
## F-statistic: 8855 on 1 and 2514 DF, p-value: < 2.2e-16
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0001325 0.0005810 0.0010162 0.0013792 0.0053893
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.002344 0.020643 0.045596 0.066930 1.095443
##
## Call:
## lm(formula = as.vector(slogcsmpperson) ~ as.vector(slogpop),
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68410 -0.26193 -0.00778 0.22293 2.87805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.382e-15 9.760e-03 0.00 1
## as.vector(slogpop) 8.700e-01 9.832e-03 88.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4895 on 2514 degrees of freedom
## (340 observations deleted due to missingness)
## Multiple R-squared: 0.757, Adjusted R-squared: 0.7569
## F-statistic: 7830 on 1 and 2514 DF, p-value: < 2.2e-16
nothing interesting
##
## Call:
## lm(formula = as.vector(slogcsmp) ~ as.vector(slogurbanpop), na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44687 -0.17856 -0.00119 0.18494 1.73333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.249e-16 7.073e-03 0.0 1
## as.vector(slogurbanpop) 9.340e-01 7.125e-03 131.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3548 on 2514 degrees of freedom
## (340 observations deleted due to missingness)
## Multiple R-squared: 0.8724, Adjusted R-squared: 0.8723
## F-statistic: 1.718e+04 on 1 and 2514 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = as.vector(slogcsmp) ~ as.vector(socioeconomic[,
## 6, ]), na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9827 -0.5487 0.1436 0.6226 2.1699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.40620 0.06360 -22.11 <2e-16 ***
## as.vector(socioeconomic[, 6, ]) 2.26459 0.09824 23.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9022 on 2514 degrees of freedom
## (340 observations deleted due to missingness)
## Multiple R-squared: 0.1745, Adjusted R-squared: 0.1742
## F-statistic: 531.4 on 1 and 2514 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = as.vector(slogcsmpperson) ~ as.vector(slogurbanpop),
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78228 -0.19990 -0.00235 0.20322 1.79857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.742e-16 7.505e-03 0.0 1
## as.vector(slogurbanpop) 9.253e-01 7.561e-03 122.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3765 on 2514 degrees of freedom
## (340 observations deleted due to missingness)
## Multiple R-squared: 0.8563, Adjusted R-squared: 0.8562
## F-statistic: 1.498e+04 on 1 and 2514 DF, p-value: < 2.2e-16